The influence of vegetation index thresholding on EO-based assessments of exposed soil masks in Germany between 1984 and 2019

Zepp, Simone; Jilge, Marianne; Metz-Marconcini, Annekatrin; Heiden, Uta

doi:10.1016/j.isprsjprs.2021.06.015

Cited by 8 publications

(11 citation statements)

References 58 publications

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“…(ii) Limitation of thresholds for bare soil detection: Recently, Dvorakova et al [76] demonstrated a set of proper thresholds, taking into account the phenological stages of crops and enabling an automatic generation of Sentinel-2 multi-temporal composites by minimizing the influence of distracting factors such as crop residues, surface roughness, and soil moisture. These findings were in concordance with the recent study of Zepp et al [78], where the influence of vegetation index thresholding on Landsat assessments of exposed soil masks was also studied. Conversely, Castaldi [79] highlighted that Sentinel-2 and Landsat-8 were not able to properly predict clay and CaCO 3 because of the low spectral resolution in the SWIR.…”

Section: Current Limitationssupporting

confidence: 93%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

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Section: Current Limitationssupporting

confidence: 93%

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

et al. 2021

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“…A different aspect that could be considered for validation is an internal quality measure provided by the number of cloudless scenes per pixel. The usable data availability can be taken into consideration for data validation [51,95]. For the calibration and the validation dataset, an analysis of the number of cloudless observations of all sampling pixels showed a similar distribution (Figure 11a,b).…”

Section: External Validationmentioning

confidence: 96%

“…Bare soil pixels are selected based on a modified vegetation index (PV) using two thresholds that allow separating predominantly undisturbed soils from all other land cover types such as permanent vegetation and permanent non-vegetation. The derivation of the thresholds is based on an automated technique described in [51]. All selected bare soil pixels are averaged.…”

Section: Scmap Src and Spectral Indicesmentioning

confidence: 99%

Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

et al. 2021

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For food security issues or global climate change, there is a growing need for large-scale knowledge of soil organic carbon (SOC) contents in agricultural soils. To capture and quantify SOC contents at a field scale, Earth Observation (EO) can be a valuable data source for area-wide mapping. The extraction of exposed soils from EO data is challenging due to temporal or permanent vegetation cover, the influence of soil moisture or the condition of the soil surface. Compositing techniques of multitemporal satellite images provide an alternative to retrieve exposed soils and to produce a data source. The repeatable soil composites, containing averaged exposed soil areas over several years, are relatively independent from seasonal soil moisture and surface conditions and provide a new EO-based data source that can be used to estimate SOC contents over large geographical areas with a high spatial resolution. Here, we applied the Soil Composite Mapping Processor (SCMaP) to the Landsat archive between 1984 and 2014 of images covering Bavaria, Germany. Compared to existing SOC modeling approaches based on single scenes, the 30-year SCMaP soil reflectance composite (SRC) with a spatial resolution of 30 m is used. The SRC spectral information is correlated with point soil data using different machine learning algorithms to estimate the SOC contents in cropland topsoils of Bavaria. We developed a pre-processing technique to address the issue of combining point information with EO pixels for the purpose of modeling. We applied different modeling methods often used in EO soil studies to choose the best SOC prediction model. Based on the model accuracies and performances, the Random Forest (RF) showed the best capabilities to predict the SOC contents in Bavaria (R² = 0.67, RMSE = 1.24%, RPD = 1.77, CCC = 0.78). We further validated the model results with an independent dataset. The comparison between the measured and predicted SOC contents showed a mean difference of 0.11% SOC using the best RF model. The SCMaP SRC is a promising approach to predict the spatial SOC distribution over large geographical extents with a high spatial resolution (30 m).

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“…The development of the database for the threshold derivation is automated. The threshold itself has been derived based on manually defined percentile measures [49].…”

Section: Scmap-srcmentioning

confidence: 99%

“…In this article, we deepen a study by Zepp et al (2021), which applied different modeling methods on Landsat-based SRC data for SOC content prediction in Bavaria, Germany [5]. As a result, the Random Forest (RF) showed the best predictive capabilities in terms of model accuracy and performance.…”

Section: Introductionmentioning

confidence: 99%

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

Möller¹,

Zepp²,

Wiesmeier³

et al. 2022

Preprint

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There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at field scale for food security, monitoring long-term changes related to soil health and climate change. In Germany, large-scale SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and Landsat-based multi-temporal soil reflectance composites (SRC) as explanatory variables. On the example of an Bavarian test of about 8000 km2, the scale-specific dependencies between the representativeness of 220 soil samples and different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters lead to a significant model improvement at large field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2=0.84 and RMSE=1.99.

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The influence of vegetation index thresholding on EO-based assessments of exposed soil masks in Germany between 1984 and 2019

Cited by 8 publications

References 58 publications

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Estimation of Soil Organic Carbon Contents in Croplands of Bavaria from SCMaP Soil Reflectance Composites

Scale-specific Prediction of Topsoil Organic Carbon Contents using Terrain Attributes and SCMaP Soil Reflectance Composites

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